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Anumber of buy-side and sell- side firms have come together
to explore using Blockchain technology could be used to improve
reference data processes.

The group, which is working
alongside technology firms R3
and Axoni through the Securities
Industry and Financial Markets
Assocation (SIFMA), has recently
complete a proof of concept
exercise that was coordinated by
Credit Suisse to build a distributed ledger prototype that would
enhance risk management, cost
and efficiency for reference data.

The concept was developed using
Axoni’s proprietary distributed
ledger software to simulate collaborative management of reference data.

Participants in the test were able to
interact with the reference data after
corporate bond issuance. Any proposed changes to the data required
validation by the underwriter to give
the ledger a single, immutable record
of all data related to the bond.

The firms claim that up to 70%
of the data used in financial transactions is reference data, underlining the importance of ensuring
such datasets are accurate.

Dean Rutter, CEO of R3, said:“Quality of data has becomea crucial issue for financialinstitutions in today’s markets.Unfortunately, their middle andback offices rely on legacy systemsand processes – often manual – tomanage and repair unclear, inac-curate reference data. Distributedledger technology – which allowsfinancial institutions to push thesefunctions to a cloud environment– removes the need to reconcilemultiple copies of data, providing asophisticated and agile solution tothe headaches currently caused bythese legacy systems and processes.”While the initial prototype sug-gests Blockchain technology couldhave applications in referencedata, it remains in its early stages,though participants in the proofof concept hope it will encouragefurther review of how it can beapplied to improve efficiencies indata and other financial servicesapplications. nReference data latest area tobenefit from Blockchain tech

Proof of concept led by Credit Suisse suggests
Blockchain tech can be applied to data.